Frank Noé - Curriculum Vitae

Born May 13, 1975, Zweibrücken, GermanyCitizenship: GermanWork Address: FU Berlin, Arnimallee 6, 14195 Berlin, Germanyphone: +49 - 30 - 838 75354e-mail: frank.noe@fu-berlin.deweb: www.research.franknoe.de

Professional appointments

2015 - Present Adjunct Professor, Dept. of Chemistry, Rice University, Houston, TX.2013 - Present Professor, FU Berlin - Joint Position between the departments of Mathematics, Physics,

and Chemistry2007 - 2013 Independent Junior Group Leader, DFG Centre MATHEON, FU Berlin2009 Visiting professor for scientific computing (W2), IWR/University of Heidelberg, Germany2006 - 2007 Postdoc, “Modelling and Simulation in the Biosciences” initiative, Heidelberg, Germany2002 - 2005 Graduate student, Interdisciplinary Centre for Scientific Computing, Heidelberg, Germany2000 - 2002 Lecturer, Computer Science, Cork Institute of Technology, Ireland1999 - 2000 Scientific software developer, Institute for New Media, Frankfurt, Germany1996 - 1999 Engineer, Robert Bosch GmbH, Stuttgart and Hildesheim, Germany

Academic education

2006 (Jan 27) Ph.D. (summa cum laude / with distinction) in Computer Science and TheoreticalBiophysics, University of Heidelberg, Germany.

2002 M.Sc. in Computing, Cork Institute of Technology, Ireland.1999 B.Sc. in Electrical Engineering (highest grade of the year), University of Cooperative

Education Stuttgart, Germany.

Institutional Roles and Graduate Programs

2015 - 2019 Initiator, Designer and Dean of Studies, Master program Computational Sciences, FU Berlin2013 - Faculty Member, Mathematics and Computer Science, FU Berlin

Associated Faculty Member, Physics and Chemistry, FU Berlin2009 - 2012 Acquired and served as a Founding Director of the CECAM node at FU Berlin2012 - Faculty Member, Berlin Mathematical School (BMS)2008 - Faculty Member, International Max Planck Research School CBSC, Berlin2006 - 2010 Mentor, DFG Graduate College 710 “Complex Processes”, University of Heidelberg

Research interests

• Machine Learning in the Sciences: Development of deep learning architectures and algorithms toaddress fundamental problems in statistical mechanics, quantum mechanics and theoretical chemistry.

• Method development and simulation in chemical physics and theoretical chemistry: Protein moleculardynamics (especially long-timescale simulation), particle-based reaction dynamics for cellular signaling,multiscale modeling between atomistic and cellular scale, integration of simulation and experimentaldata.

• Scientific software development for the open-source dissemination of our methods: Dissemination ofresearch in community-usable software, modular software design, Open source software in Python, C,C++, Java.

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Offers and Awards

• 2019 - 2020 ISI Highly Cited Researcher (Web of Science)

• 2019 Simons Fellow at the Institute of Pure and Applied Mathematics (IPAM)

• 2019 Early-Career Award in Theoretical Chemistry of the Americal Chemical Society (ACS)

• 2018 - 2022 ERC consolidator grant (awarded 2017)

• 2013 Won FU-Berlin internal competition for funding and installation of a new junior faculty group inthe field of High-Performance Computing (now hired: Prof. Felix Höfling)

• 2013 - 2017 ERC starting grant (awarded 2012)

• 2010 Member of the “Genshagener Kreis”, a program for selected junior scientists of the EinsteinFoundation Berlin

• 2009 Director of the CECAM node Berlin

• 2007 Fellow, “elite program for postdocs”, Federal Foundation of Baden-Württemberg

• 2006 Offered permanent PI position at Oak Ridge National Lab., TN, USA (not realized)

• 2006 Postdoctoral stipend, “Modelling and Simulation in the Biosciences” (BIOMS) initiative (3 yearsoffered, 1 year realized)

• 2000 - 2002 CIT Cork MSc research stipend

Scientific memberships and editorial activities

• Fellow, European Laboratory for Learning and Intelligent Systems (ELLIS)

• Editor, Machine Learning: Science and Technology (IOPscience)

• Editorial Advisory Board, Journal of Chemical Theory and Computation (ACS)

• Member: American Physical Society, American Chemical Society, Biophysical Society.

Workshops organized (including fundraising)

• 2019 “Learning Physics and the Physics of Learning”, Long Program, IPAM, UCLA, CA

• 2017 “Surrogate Models and Coarsening Techniques”, IPAM, UCLA, CA (co-organizer)

• 2016 “Workshop on Kinetics and Markov State Models in Drug Design”, Boston, MA (co-organizer)

• 2009,11,13,15 “Molecular Kinetics”, FU Berlin, 100 participants (raised ca. 40K EUR each)

• 2003,05,06 “Methods of Molecular Simulation”, Heidelberg, 30-80 participants, (raised 5K EUR each)

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Funding from competitively evaluated grants (total: 9,033,000 EUR direct costs)

Duration Active Grant EUR (total, direct costs)2019 - 20212020 - 20222018 - 20212018 - 20222017 - 20192016 - 20202015 - 20182015 - 20172011 - 2019

MATH+ Excellence ClusterDFG grant NO 825/3-2DFG GRK DAEDALUSERC consolidator grantDFG research group 2518DFG transregio 186DFG collaborative research center 1114DFG grant NO 825/2-2DFG collaborative research center 958

555 000250 000180 000

1 600 000380 000352 000450 000180 000660 000

Duration Previous Grant EUR (total, direct costs)2013 - 20172013 - 20172011 - 20142010 - 20182009 - 20112009 - 20102008 - 20112008 - 20102007 - 20142007 - 2008

Einstein foundation postdoc fellowshipERC starting grantDFG grant NO 825/3-1DFG collaborative research center 740DFG grant NO 825/2-1CECAM node FU BerlinDFG collaborative research center 449Center of Scientific SimulationDFG research center “Matheon”Elite program Landesstiftung Baden-Württemberg

367 0001 395 000285 000950 000200 00080 000

180 00067 000

840 00062 000

Postdoctoral fellows (current and alumni)1. A. Kraemer: Generative machine learning molecular dynamics sampling2. E. Abualrous: Experiment and simulation, immune system3. S. Kaptan: Modeling of ion channels4. L. Raich: Computational drug design in collaboration with Bayer AG5. M. Sadeghi: Membrane mechanics, software development6. S. Stolzenberg: High-performance molecular dynamics, Markov modeling (won a temporary Principal

Investigator position of the German Science Foundation)7. B. Husic: Machine learning for coarse-grained molecular dynamics8. M. Entwistle: Machine learning for quantum Chemistry9. J. Hermann (now Group Leader at the Berlin Institute for Learning and Data)

10. S. Olsson (now Assistant Professor at Chalmers University Gothenborg SE. Won a HumboldtPostdoctoral fellowship)

11. J. Zhang (now Postdoc in Peking University. Won a Humboldt Postdoctoral fellowship)12. H. Wu (now Assistant Professor at Tongji University, Shanghai. Won fellowship in the 1000 talents

program of the Chinese government)13. S. Bernhard (now Professor at Hochschule Pforzheim)14. B. G. Keller (now tenured Associate Professor at FU Berlin)15. A. S. J. S. Mey (now Christina Miller Fellow at University of Edinburgh)16. G. Pérez-Hernández: (now Postdoc at Charité Berlin)17. N. Plattner: (now at University of Bern)18. J.-H. Prinz (now Data Scientist at keylight GmbH)19. M. del Razo Sarmina (now independent Postdoc at University of Amsterdam)20. R. Shevchuk (now Postdoc at University of Göttingen)21. A. Ullrich (now Freelancer)22. C. Wehmeyer (now Data Scientist at Statice)

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Ph.D. students and supervised theses1. S. Bandstra (1st year): Machine Learning for super-resolution microscopy2. Y. Chen (1st year): Machine Learning for implicit solvation3. M. Dibak (4th year): Graphical models, multiscale simulation4. K. Elez (2nd year): Machine Learning, cryo-EM reconstruction5. T. Hempel (4th year): Molecular kinetics, Markov modeling6. M. Hoffmann (4th year): Sparse learning, software development7. J. Köhler (2nd year): Deep learning theory8. L. Klein (2nd year): Deep generative models9. T. Le (2nd year): Machine learning for drug design

10. A. Mardt (4rd year): Deep learning for dynamical systems11. Z. Schätzle (1st year): Deep learning for quantum mechanics12. R. Winter (3rd year): Deep learning for drug development. Collaboration with Bayer AG.13. C. Fröhner (now software developer at IVU traffic technologies): Reaction-diffusion systems in and

out of equilibrium, PhD thesis, 2019.14. L. Sbailò (now postdoc at Fritz-Haber Institute): Efficient multi-scale sampling methods in statistical

physics, PhD thesis, 2019.15. F. Paul (now postdoc at University of Chicago): Markov State Modeling of Binding and Conforma-

tional Changes of Proteins, PhD thesis, 2017 (Awarded with summa cum laude, Carl Ramsauer Preisder physikalischen Gesellschaft zu Berlin 2018).

16. F. Nüske (now postdoc at Paderborn University): The Variational Approach to Conformational Dy-namics, PhD thesis, FU Berlin, 2017 (Awarded with: summa cum laude, Tiburtius dissertation Prize,RAUF postdoctoral fellowship at Rice University, Houston, TX).

17. B. Trendelkamp-Schroer: Reversible Markov state models, PhD thesis, FU Berlin, 2016.18. F. Vitalini (now at Mirai Solutions Zürich): Hierarchical Approaches to kinetic models of peptides,

PhD thesis, FU Berlin, 2015.19. J. Schöneberg (now Assistant Professor at UC San Diego, CA): Reaction-diffusion dynamics in bio-

logical systems. PhD thesis, FU Berlin, 2014 (Awarded with: summa cum laude, Tiburtius dissertationPrize, Marie Curie Fellowship).

20. J. H. Prinz (now at keylight GmbH): Advanced estimation methods for Markov models of dynamicalsystems. PhD thesis, FU Berlin, 2012 (Awarded with summa cum laude).

21. M. Held (now at HitFox Berlin): Novel concepts for conformation and association dynamics in bio-molecules. PhD thesis, FU Berlin, 2012 (Awarded with summa cum laude).

Supervised M.Sc., Diploma and B.Sc. theses1. Y. Chen: Extensible and transferable deep learning architectures for implicit solvent models, MSc

Thesis in Comput. Sciences, FU Berlin, 20202. B. Miller: SE(3) Equivariant Neural Networks for Regression on Molecular Properties, MSc Thesis in

Comput. Sciences, FU Berlin, 20203. V. Wolf: EwaldBlocks: Translating the Idea of Ewald Summation to Neural Networks for Global and

Local Feature Extraction, BSc Thesis in Computer Science, FU Berlin (2020)4. L. Klein: Exploring new Monte-Carlo Algorithms with Deep Learning Methods, MSc Thesis in Physics,

FU Berlin, 2019.5. Z. Schätzle: Solving the Schrödinger Equation with Deep Learning, MSc Thesis in Physics, FU Berlin,

2019.6. M. Salomon: Predicting binding affinities of protein-ligand complexes using SchNet, MSc Thesis in

Comp. Sciences, FU Berlin, 2019.7. F. Litzinger: Sparse approximation of generalized eigenvalue problems, MSc Thesis in Mathematics,

FU Berlin, 2017.8. L. Deecke: Supervised learning of quantum chemical properties, MSc Thesis in Physics, FU Berlin,

2017. (Awarded with Study Prize of the Berlin Physical Society 2018)

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9. C. Fröhner: Reversible integrators for particle based reaction-diffusion, MSc Thesis in Physics, FUBerlin, 2015.

10. K. Colditz: Adaptive Discretization for Markov Model Construction using HMM, MSc Thesis inMathematics, FU Berlin, 2015.

11. J. Biedermann: Accelerating interacting-particle reaction-diffusion simulations using graphical pro-cessing units, MSc Thesis in Bioinformatics, FU Berlin, 2015.

12. C. Schaller: STORM microscopy - a mathematical analysis, MSc Thesis in Mathematics, FU Berlin,2013.

13. F. Nüske: A variational approach for conformation dynamics, MSc Thesis in Mathematics, FU Berlin,2012.

14. J. Seibert: Investigation of the reaction-diffusion processes of rod cell disc membrane photoactivationwith single-particle resolution, MSc Thesis in Bioinformatics, FU Berlin, 2010.

15. M. Kavalar: Conformational Dynamics of a Peptide Ligand in Solvent and in Complex with a MHC-IProtein, Diploma thesis in Physics, FU Berlin, 2010.

16. A. Azhand: Diffusion Processes with Hidden States from Single Molecule Experiments, Diploma thesisin Physics, TU Berlin, 2009.

17. T. Lüdge: A theoretical model of conformational transitions in biomolecules based on single-moleculeFörster resonance electron transfer measurements, Diploma thesis in Physics, TU Berlin, 2009.

18. M. Fischbach: Methods for modeling metastable conformational dynamics from trajectory data, Dip-loma thesis in Computer Science, FU Berlin, 2008.

19. I. Sakalli: Parallelization Strategies for a Brownian Dynamics Algorithm”, BSc thesis in Bioinformatics,FU Berlin, 2008.

20. M. Held: Conformational Studies of UDP-N-Acetyl-Glucosamine in Environments of Increasing Com-plexity ”, MSc thesis in Bioinformatics, FU Berlin, 2007.

21. D. Skanda: Estimation of equilibrium probabilities of molecular conformations from non-overlappingsimulations”, Diploma thesis in Physics, Univ. Heidelberg, 2006.

Citation statistics (based on Google scholar)

Number of citations: 11914(Mar 3, 2021)

h-index: 54

200809 10 11 12 13 14 15 16 17 18 19 200500

10001500200025003000

citations per year

Books

1. G. Bowman, F. Noé and V. Pande (Editors): “An Introduction to Markov State Models and Their Ap-plication to Long Timescale Molecular Simulation.”, Advances in Experimental Medicine and Biology,797. Springer, Heidelberg (2014)

Journal articles

1. J. Hermann, Z. Schätzle and F. Noé. Deep-neural-network solution of the electronic Schrödingerequation. Nature Chem. 12, 891-897 (2020)

2. T. Hempel, L. Raich, S. Olsson, N. P. Azouz, A. M. Klingler, M. E. Rothenberg and F. Noé: Molecularmechanism of SARS-CoV-2 cell entry inhibition via TMPRSS2 by Camostat and Nafamostat mesylate.Chem. Sci. (in press) DOI: 10.1039/D0SC05064D.

3. T. Le, R. Winter, F. Noé and D.-A. Clevert. Neuraldecipher-Reverse-engineering extended-connectivityfingerprints (ECFPs) to their molecular structures. Chem. Sci. 11, 10378-10389 (2020)

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4. M. Sadeghi and F. Noé. Large-scale simulation of biomembranes: bringing realistic kinetics to coarse-grained models Nat. Commun. 11, 1-13 (2020)

5. B. E. Husic, N. E. Charron, D. Lemm, J. Wang, A. Pérez, A. Krämer, Y. Chen, S. Olsson, G. deFabritiis, F. Noé and C. Clementi. Coarse Graining Molecular Dynamics with Graph Neural Networks.J. Chem. Phys. 153, 194101 (2020)

6. J. Wang, S. Chmiela, K.-R. Müller, F. Noé and C. Clementi. Ensemble learning of coarse-grainedmolecular dynamics force fields with a kernel approach. J. Chem. Phys. 152, 194106 (2020).

7. R. Winter, J. Retel, F. Noé, D. A. Clevert, A. Steffen. grünifai: Interactive multi-parameter optimiz-ation of molecules in a continuous vector space Bioinformatics 36, 4093-4094 (2020)

8. F. Noé, A. Tkatchenko, K.-R. Müller, C. Clementi. Machine learning for molecular simulation. Ann.Rev. Phys. Chem. 71, 361-390 (2020)

9. T. Hempel, N. Plattner and F. Noé. Coupling of conformational switches in calcium sensor unraveledwith local Markov models and transfer entropy. J. Chem. Theory Comput. 16, 2584-2593 (2020)

10. N. K. Singh, E. T. Abualrous, C. M. Ayres, F. Noé, R. Gowthaman, B. G. Pierce, B. M. Baker.Geometrical characterization of T cell receptor binding modes reveals class-specific binding to maximizeaccess to antigen. Proteins 88, 503-513 (2020)

11. F. Noé, G. De Fabritiis, C. Clementi. Machine learning for protein folding and dynamics. Curr. Opin.Struct. Biol. 60, 77-84 (2020)

12. S. Klus, B. E. Husic, M. Mollenhauer and F. Noé. Kernel methods for detecting coherent structuresin dynamical data. Chaos 29, 123112 (2019).

13. F. Noé and E. Rosta. Markov Models of Molecular Kinetics. J. Chem. Phys. 151, 190401 (2019).

14. J. K. Noel, F. Noé, O. Daumke and A. S. Mikhailov: Polymer-like model to study the dynamics ofdynamin filaments on deformable membrane tubes. Biophys. J. 117, 1870-1891 (2019).

15. M. Lehmann, I. Lukonin, F. Noé, J. Schmoranzer, C. Clementi, D. Loerke and V. Haucke: Nanoscalecoupling of endocytic pit growth and stability. Sci. Adv. 5, eaax5775 (2019)

16. M. Dibak, C. Fröhner, F. Noé, F. Höfling. Diffusion-influenced reaction rates in the presence of pairinteractions. J. Chem. Phys. 151, 164105 (2019).

17. N. K. Singh, E. T. Abualrous, C. M. Ayres, F. Noé, R. Gowthaman, B. G. Pierce and B. M. Baker.Geometrical characterization of T cell receptor binding modes reveals class-specific binding to maximizeaccess to antigen. Proteins doi:10.1002/prot.25829 (2019).

18. J. Zhang, Y. I. Yang, F. Noé. Targeted adversarial learning optimized sampling. J. Phys. Chem.Lett. 10, 5791-5797 (2019).

19. F. Noé, S. Olsson, J. Köhler, H. Wu. Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning. Science 365, eaaw1147 (2019).

20. B. E. Husic, F. Noé. Deflation reveals dynamical structure in nondominant reaction coordinates. J.Chem. Phys. 151, 054103 (2019).

21. H. Wu and F. Noé. Variational approach for learning Markov processes from time series data. J.Nonlinear Sci., 30, 23-66 (2019).

22. S. Olsson, F. Noé. Dynamic graphical models of molecular kinetics. Proc. Natl. Acad. Sci. USA,116, 15001-15006 (2019).

23. K. Faelber, L. Dietrich, J. K. Noel, F. Wollweber, A. K. Pfitzner, A. Mühleip, R. Sánchez, M.Kudryashev, N. Chiaruttini, H. Lilie, J. Schlegel, E. Rosenbaum, M. Hessenberger, C. Matthaeus, S.Kunz, A. von der Malsburg, F. Noé, A. Roux, M. van der Laan, W. Kühlbrandt and O. Daumke:Structure and assembly of the mitochondrial membrane remodelling GTPase Mgm1. Nature 571,429-433, (2019).

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24. M. K. Scherer, B. E. Husic, M. Hoffmann, F. Paul, H. Wu, F. Noé: Variational selection of featuresfor molecular kinetics. J. Chem. Phys. 150, 194108 (2019).

25. R. Winter, F. Montanari, A. Steffen, H. Briem, F. Noé, D. A. Clevert. Efficient multi-objectivemolecular optimization in a continuous latent space. Chem. Sci. 10, 8016-8024 (2019)

26. F. Paul, H. Wu, M. Vossel, B. L. de Groot and F. Noé. Identification of kinetic order parameters fornon-equilibrium dynamics. J. Chem. Phys. 150, 164120 (2019).

27. J. Wang, S. Olsson, C. Wehmeyer, A. Pérez, N. E. Charron, G. De Fabritiis, F. Noé and C. Clementi:Machine learning of coarse-grained molecular dynamics force fields. ACS Central Sci. 5, 755-767(2019).

28. C. Ayres, E. Abualrous, A. Bailey, C. A. Arega, L. H. Hellman, S. Corcelli, F. Noé, T. Elliott and B.M. Baker: Dynamically driven allostery in MHC proteins: peptide-dependent tuning of class I MHCglobal flexibility. Frontiers Immunol. 10, 966 (2019).

29. E. Dimou, K. Cosentino, E. Platonova, U. Ros, M. Sadeghi, P. Kashyap, T. Katsinelos, S. Wegehingel,F. Noé, A. J. García-Sáez, H. Ewers and W. Nickel. Single event visualization of unconventionalsecretion of FGF2. J. Cell. Biol. 218, 683-699 (2019).

30. M. Hoffmann, C. Fröhner and F. Noé. ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics. PLoS Comput. Biol. 15, e1006830 (2019).

31. M. Hoffmann, C. Fröhner and F. Noé: Reactive SINDy: Discovering governing reactions from con-centration data. J. Chem. Phys. 150, 025101 (2019).

32. J. Kappler, F. Noé and R. R. Netz. Cyclization and Relaxation Dynamics of Finite-Length CollapsedSelf-Avoiding Polymers. Phys. Rev. Lett. 122, 067801 (2019).

33. G. Pinamonti, F. Paul, F. Noé, A. Rodriguez and G. Bussi: The mechanism of RNA base fraying:Molecular dynamics simulations analyzed with core-set Markov state models. J. Chem. Phys. 150,154123 (2019).

34. R. Winter, F. Montanari, F. Noé, D. A. Clevert. Learning continuous and data-driven moleculardescriptors by translating equivalent chemical representations. Chem. Sci. 10, 1692-1701 (2019).

35. M. J. del Razo, H. Qian and F. Noé: Grand canonical diffusion-influenced reactions: a stochastictheory with applications to multiscale reaction-diffusion simulations. J. Chem. Phys. 149, 044102(2018).

36. M. Dibak, M. J. del Razo, D. De Sancho, C. Schütte and F. Noé: MSM/RD: Coupling Markov statemodels of molecular kinetics with reaction-diffusion simulations. J. Chem. Phys. 148, 214107(2018).

37. C. Fröhner, F. Noé: Reversible Interacting-Particle Reaction Dynamics. J. Phys. Chem. B 122,11240-11250 (2018)

38. S. Gerber, S. Olsson, F. Noé and I. Horenko: A scalable approach to the computation of invariantmeasures for high-dimensional Markovian systems. Sci. Rep. 8, 1796 (2018).

39. S. Klus, F. Nüske, P. Koltai, H. Wu, I. Kevrekidis, C. Schütte and F. Noé: Data-driven model reductionand transfer operator approximation. J. Nonlin. Dyn. 28, 985-1010 (2018)

40. P. Koltai, H. Wu, F. Noé and C. Schütte: Optimal data-driven estimation of generalized Markov statemodels for non-equilibrium dynamics. Computation 6, 22 (2018)

41. F. Litzinger, L. Boninsegna, H. Wu, F. Nüske, R. Patel, R. Baraniuk, F. Noé and C. Clementi: Rapidcalculation of molecular kinetics using compressed sensing. J. Chem. Theory Comput. 14, 2771-2783 (2018).

42. A. Mardt, L. Pasquali, H. Wu and F. Noé: VAMPnets: Deep learning of molecular kinetics. Nat.Commun. 9, 5 (2018)

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43. F. Paul, F. Noé and T. R. Weikl: Identifying Conformational-Selection and Induced-Fit Aspects inthe Binding-Induced Folding of PMI from Markov State Modeling of Atomistic Simulations. J. Phys.Chem. B. 122, 5649-5656 (2018).

44. B. M. Qureshi, E. Behrmann, J. Schöneberg, J. Loerke, J. Bürger, T Mielke, J. Giesebrecht, F. Noé,T. D Lamb, K. P. Hofmann, C. M. T. Spahn, M. Heck: It takes two transducins to activate thecGMP-phosphodiesterase 6 in retinal rods Open Biol. 8, 180075 (2018)

45. M. Sadeghi, T. R. Weikl and F. Noé: Particle-based membrane model for mesoscopic simulation ofcellular dynamics. J. Chem. Phys. 148, 044901 (2018).

46. R. Schulz, Y. von Hansen, J. O. Daldrop, J. Kappler, F. Noé and R. R. Netz. Collective hydrogen-bondrearrangement dynamics in liquid water. J. Chem. Phys. 149, 244504 (2018).

47. D. W. H. Swenson, J.-H. Prinz, F. Noé, J. D. Chodera and P. G. Bolhuis: OpenPathSampling: APython framework for path sampling simulations. I. Basics J. Chem. Theory Comput. 15, 813-836(2018).

48. D. W. H. Swenson, J.-H. Prinz, F. Noé, J. D. Chodera and P. G. Bolhuis. OpenPathSampling: APython Framework for Path Sampling Simulations. II. Building and Customizing Path Ensembles andSample Schemes J. Chem. Theory Comput. 15, 837-856 (2018).

49. C. Wehmeyer and F. Noé: Time-lagged autoencoders: Deep learning of slow collective variables formolecular kinetics. J. Chem. Phys. 148, 241703 (2018)

50. C. Wehmeyer, M. K. Scherer, T. Hempel, B. E. Husic, S. Olsson and F. Noé: Introduction to Markovstate modeling with the PyEMMA software v1.0 LiveCoMS 1, 1-12, (2018)

51. F. Paul, C. Wehmeyer, E. T. Abualrous, H. Wu, M. D. Crabtree, J. Schöneberg, J. Clarke, C. Freund,T. R. Weikl and F. Noé: Protein-peptide association kinetics beyond the seconds timescale fromatomistic simulations. Nat. Commun. 8, 1095 (2017).

52. N. Plattner, S. Doerr, G. De Fabritiis and F. Noé: Complete protein-protein association kinetics inatomic detail revealed by molecular dynamics simulations and Markov modeling. Nat. Chem. 9,1005-1011 (2017).

53. J. Schöneberg, M. Lehmann, A. Ullrich, Y. Posor, W.-T. Lo, G. Lichtner, J. Schmoranzer, V. Hauckeand F. Noé: Lipid-mediated PX-BAR domain recruitment couples local membrane constriction toendocytic vesicle fission. Nat. Commun. 8, 15873 (2017).

54. S. Olsson, H. Wu, F. Paul, C. Clementi and F. Noé: Combining experimental and simulation data ofmolecular processes via augmented Markov models. Proc. Natl. Acad. Sci. USA, 114, 8265-8270(2017).

55. F. Noé and C. Clementi: Collective variables for the study of long-time kinetics from moleculartrajectories: theory and methods. Curr. Opin. Struct. Biol. 43, 141-147 (2017).

56. A. M. Abramyan, S. Stolzenberg, Z. Li, C. J. Loland, F. Noé and L. Shi: The isomeric preference ofan atypical dopamine transporter inhibitor contributes to its selection of the transporter conformation.ACS Chem. Neurosc. 8, 1735-1746 (2017).

57. L. Sbailò and F. Noé: An efficient multi-scale Green’s function reaction dynamics scheme. J. Chem.Phys. 147, 184106 (2017)

58. H. Wu, F. Nüske, F. Paul, S. Klus, P. Koltai and F. Noé: Variational Koopman models: Slow collectivevariables and molecular kinetics from short off-equilibrium simulations. J. Chem. Phys. 146, 154104(2017).

59. M. Wieczorek, E. T. Abualrous, J. Sticht., M. Álvaro-Benito, S. Stolzenberg, F. Noé and C. Fre-und: Major Histocompatibility Complex (MHC) Class I and MHC Class II Proteins: ConformationalPlasticity in Antigen Presentation. Front. Immunol. 8, 292 (2017).

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60. F. Nüske, H. Wu, C. Wehmeyer, C. Clementi and F. Noé Markov State Models from short non-Equilibrium Simulations - Analysis and Correction of Estimation Bias. J. Chem. Phys. 094104(2017).

61. S. Olsson and F. Noé: Mechanistic models of chemical exchange induced relaxation in protein NMR.J. Am. Chem. Soc. 139, 200–210 (2017).

62. G. Pinamonti, J. Zhao, D. Condon, F. Paul, F. Noé, D. Turner and G. Bussi, Predicting the kin-etics of RNA oligonucleotides using Markov state models. J. Comp. Theory Comput. DOI:10.1021/acs.jctc.6b00982 (2017).

63. D. Albrecht, C. M. Winterflood, T. Tschager, F. Noé and H. Ewers, Nanoscopic compartmentalizationof membrane protein motion at the axon initial segment. J. Cell Biol. 215, 37-46 (2016).

64. S. Doerr, M. J. Harvey, F. Noé and G. De Fabritiis, HTMD: High-Throughput Molecular Dynamicsfor Molecular Discovery. J. Chem. Theory Comput. 12, 1845-1852 (2016).

65. F. Nüske, R. Schneider, F. Vitalini and F. Noé Variational Tensor Approach for Approximating theRare-Event Kinetics of Macromolecular Systems. J. Chem. Phys. 144, 054105 (2016).

66. F. Noé and R. Banisch and C. Clementi, Commute maps: separating slowly-mixing molecular config-urations for kinetic modeling. J. Chem. Theory Comput. 12, 5620-5630 (2016).

67. G. Pérez-Hernández, F. Noé: Hierarchical time-lagged independent component analysis: computingslow modes and reaction coordinates for large molecular systems. J. Chem. Theory Comput. 12,6118-6129 (2016)

68. B. Trendelkamp-Schroer and F. Noé Efficient estimation of rare-event kinetics. Phys. Rev. X, 6 .011009 (2016).

69. F. Vitalini, F. Noé and B. Keller, Molecular dynamics simulations data of the twenty encoded aminoacids in different force fields. Data in Brief 7, 582-590 (2016).

70. M. Wieczorek, J. Sticht, S. Stolzenberg, S. Günther, C. Wehmeyer, Z. El Habre, M. Àlvaro-Benito,F. Noé and C. Freund, MHC class II complexes sample intermediate states along the peptide exchangepathway. Nat. Commun. 7, 13224 (2016).

71. H. Wu, F. Paul, C. Wehmeyer and F. Noé, Multiensemble Markov models of molecular thermody-namics and kinetics. Proc. Natl. Acad. Sci. USA 113. E3221-E3230 (2016)

72. J. Biedermann, A. Ullrich, J. Schöneberg, and F. Noé, ReaDDyMM: fast interacting-particle reaction-diffusion simulations using graphical processing units. Biophys. J. 108, 457-461 (2015)

73. L. Boninsegna, G. Gobbo, F. Noé and C. Clementi. Investigating Molecular Kinetics by VariationallyOptimized Diffusion Maps. J. Chem. Theory Comput. 11, 5947-5960 (2015)

74. M. Gunkel, J. Schöneberg, W. Alkhaldi, S. Irsen, F. Noé, U.B. Kaupp and Al-Amoudi, A: Higher-orderarchitecture of rhodopsin in intact photoreceptors and its implication for phototransduction kinetics.Structure 23, 628-638 (2015).

75. F. Noé: Beating the Millisecond Barrier in Molecular Dynamics Simulations. Biophys J., 108, 228-229 (2015).

76. F. Noé and C. Clementi: Kinetic distance and kinetic maps from molecular dynamics simulation. J.Chem. Theory Comput. 11, 5002-5011 (2015)

77. N. Plattner and F. Noé (2015) Protein conformational plasticity and complex ligand-binding kineticsexplored by atomistic simulations and Markov models. Nat. Commun. 6, 7653 (2015).

78. T. R. Reubold, K. Faelber, N. Plattner, Y. Posor, K. Branz, U. Curth, J. Schlegel, R. Anand, D.Manstein, F. Noé, V. Haucke, O. Daumke, S. Eschenburg: Crystal structure of the dynamin tetramer.Nature 525, 404-408 (2015).

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79. M. K. Scherer, B. Trendelkamp-Schroer, F. Paul, G. Pérez-Hernández, M. Hoffmann, N. Plattner, C.Wehmeyer, J.-H. Prinz and F. Noé: PyEMMA 2: A Software Package for Estimation, Validation, andAnalysis of Markov Models. J. Chem. Theory Comput. 11, 5525-5542 (2015).

80. J. Schöneberg, K.P. Hofmann, M. Heck and F. Noé: Response to Comment “Transient Complexesbetween Dark Rhodopsin and Transducin: Circumstantial Evidence or Physiological Necessity?” by D.Dell’Orco and K.-W. Koch. Biophysical J. 108, 778-779 (2015)

81. M. Schror, A.S.J.S. Mey, F. Noé and C. MacPhee: Shedding Light on the Dock-Lock Mechanism inAmyloid Fibril Growth Using Markov State Models. J. Chem. Phys. Lett. 6, 1076-1081 (2015)

82. B. Trendelkamp-Schroer, H. Wu, F. Paul and F. Noé: Estimation and uncertainty of reversible Markovmodels. J. Chem. Phys. 143, 174101 (2015)

83. A. Ullrich, M. A. Böhme, J. Schöneberg, H. Depner, S. J. Sigrist and F. Noé: Dynamical organizationof Syntaxin-1A at the presynaptic active zone. PLoS Comput. Biol. 11 e1004407 (2015).

84. F. Vitalini, A.S.J.S. Mey, F. Noé, B.G. Keller: Dynamic Properties of Force Fields. J. Chem. Phys.142, 084101 (2015).

85. F. Vitalini, F. Noé and B. G. Keller, B.: A basis set for peptides for the variational approach toconformational kinetics. J. Chem. Theory Comput. 11, 3992-4004 (2015).

86. H. Wu and F. Noé: Gaussian Markov transition models of molecular kinetics. J. Chem. Phys. 142,084104 (2015).

87. H. Wu, J.-H. Prinz and F. Noé Projected Metastable Markov Processes and Their Estimation withObservable Operator Models. J. Chem. Phys. 143, 144101 (2015).

88. J.D. Chodera and F. Noé: Markov state models of biomolecular conformational dynamics. Curr.Opin. Struct. Biol. 25, 135-144 (2014).

89. B.G. Keller, A. Kobitski, A. Jäschke, G.U. Nienhaus and F. Noé: Complex RNA folding kineticsrevealed by single molecule FRET and hidden Markov models. J. Am. Chem. Soc. 136, 4534-4543(2014)

90. A.S.J.S. Mey, H. Wu and F. Noé: xTRAM: Estimating equilibrium expectations from time-correlatedsimulation data at multiple thermodynamic states. Phys. Rev. X. 4. 041018 (2014).

91. F. Nüske, B.G. Keller, G. Pérez-Hernández, A.S.J.S. Mey and F. Noé: Variational approach to mo-lecular kinetics. J. Chem. Theory. Comput. 10, 1739-1752 (2014).

92. J.-H. Prinz, J.D. Chodera and F. Noé: Spectral rate theory for two-state kinetics Phys. Rev. X 4,011020 (2014).

93. J. Schöneberg, M. Heck, K.-P. Hofmann and F. Noé: Explicit Spatio-temporal Simulation of Receptor-G Protein Coupling in Rod Cell Disk Membranes. Biophys. J., 107, 1042-1053 (2014)

94. J. Schöneberg, A. Ullrich and F. Noé: Simulation tools for particle-based reaction-diffusion dynamicsin continuous space. BMC Biophysics 7, 11 (2014)

95. H. Wu, A.S.J.S. Mey, E. Rosta and F. Noé: Statistically optimal analysis of state-discretized trajectorydata from multiple thermodynamic states. J. Chem. Phys. 141, 214106 (2014).

96. H. Wu and F. Noé: Optimal estimation of free energies and stationary densities from multiple biasedsimulations. SIAM Multiscale Model. Simul. 12, 25-54 (2014)

97. B. Lindner, Z. Yi, J.-H. Prinz, J.C. Smith and F. Noé: Dynamic Neutron Scattering from Conforma-tional Dynamics I: Theory and Markov models. J. Chem. Phys. 139, 175101 (2013)

98. F. Noé and F. Nüske: “A variational approach to modeling slow processes in stochastic dynamicalsystems”, SIAM Multiscale Model. Simul. 11, 635-655 (2013)

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99. F. Noé, H. Wu, J.-H. Prinz and N. Plattner: Projected and Hidden Markov Models for calculatingkinetics and metastable states of complex molecules. J. Chem. Phys. 139, 184114 (2013)

100. G. Pérez-Hernández, F. Paul, T. Giogino, G. De Fabritiis and F. Noé: Identification of slow molecularorder parameters for Markov model construction. J. Chem. Phys. 139. 015102 (2013).

101. S. Peuker, A. Cukkemane, M. Held, F. Noé, U.B. Kaupp, R. Seifert: “Kinetics of ligand-receptorinteraction reveals an induced-fit mode of binding in a cyclic nucleotide-activated protein” Biophys.J. 104, 63-74 (2013).

102. Y. Posor, M. Eichhorn-Gruenig, D. Puchkov, J. Schöneberg, A. Ullrich, A. Lampe, R. Müller, S.Zarbakhsh, C. Schultz, J. Schmoranzer, F. Noé, V. Haucke: “Spatiotemporal Control of Endocytosisby Phosphatidylinositol 3,4- Bisphosphate” Nature 499, 233-237 (2013).

103. J. Schöneberg and F. Noé: ReaDDy - a software for particle based reaction diffusion dynamics incrowded cellular environments. PLoS ONE. 8, e74261 (2013)

104. K. Steger, S. Bollmann, F. Noé and S. Doose: Systematic evaluation of fluorescence correlationspectroscopy data analysis on the nanosecond time scale. Phys. Chem. Chem. Phys. 15,10435-10445 (2013)

105. B. Trendelkamp-Schroer and F. Noé: Efficient Bayesian estimation of Markov model transition matriceswith given stationary distribution. J. Phys. Chem. 138, 164113 (2013)

106. Z. Yi, B. Lindner and J.-H. Prinz and F. Noé and J.C. Smith: Dynamic Neutron Scattering fromConformational Dynamics II: Application using Molecular Dynamics Simulation and Markov modeling.J. Chem. Phys. 139, 175102 (2013)

107. M. Held, P. Imhof, B. Keller and F. Noé: “Modulation of a ligand’s energy landscape and kinetics bythe chemical environment” J. Phys. Chem. 116, 13597-13607 (2012).

108. M. Held and F. Noé: “Calculating kinetics and pathways of protein-ligand association” Eur. J. CellBiol. 91, 357-364 (2012).

109. B. Keller, J.-H. Prinz and F. Noé: “Markov models and dynamical fingerprints: unraveling the com-plexity of molecular kinetics” Chem. Phys. 396, 29-107 (2012).

110. K. Faelber, M. Held, S. Gao, Y. Posor, V. Haucke, F. Noé and O. Daumke: “ Structural insights intodynamin-mediated membrane fission” Structure 20, 1621-1628 (2012).

111. K. Sadiq, F. Noé and G. de Fabritiis: Kinetic characterization of the critical step in HIV-1 proteasematuration, Proc. Natl. Acad. Sci. USA 109, 20449-20454 (2012)

112. M. Senne, B. Trendelkamp-Schroer, A.S.J.S. Mey, C. Schütte and F. Noé: “EMMA - A softwarepackage for Markov model building and analysis”, J. Chem. Theory Comput. 8, 2223-2238 (2012).

113. J.D. Chodera, W.C. Swope, F. Noé, J.-H. Prinz and V.S. Pande: Dynamical reweighting: Improvedestimates of dynamical properties from simulations at multiple temperatures J. Chem. Phys. 134,244107 (2011).

114. K. Faelber, Song Gao, M. Held, D. Schulze, Y. Roske, F. Noé and O. Daumke: “Crystal structure ofnucleotide-free dynamin in the oligomerized state” Nature 477, 556-560 (2011).

115. F. Noé, S. Doose, I. Daidone, M. Löllmann, J.D. Chodera, M. Sauer and J.C. Smith: “Dynamicalfingerprints for probing individual relaxation processes in biomolecular dynamics with simulations andkinetic experiments”, Proc. Natl. Acad. Sci. USA 108, 4822-4827 (2011).

116. M. Held, P. Metzner, J.-H. Prinz and F. Noé: “Mechanisms of protein-ligand association and itsmodulation by protein mutations”, Biophys. J. 100, 701-710 (2011).

117. J.-H. Prinz, H. Wu, M. Sarich, B. Keller, M. Senne, M. Held, J.D. Chodera, C. Schütte, J.D. Choderaand F. Noé: “Markov models of molecular kinetics: Generation and Validation”, J. Chem. Phys.134, 174105 (2011)

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118. J.-H. Prinz, M. Held, J.C. Smith and F. Noé: “Efficient Computation, Sensitivity and Error Analysisof Committor Probabilities for Complex Dynamical Processes”, Multiscale Model. Simul. 9, 545(2011)

119. J.-H. Prinz, J.D. Chodera, V.S. Pande, W.C. Swope, J.C. Smith and F. Noé: “Optimal use of datain parallel tempering simulations for the construction of discrete-state Markov models of biomoleculardynamics”, J. Chem. Phys. 134, 244108 (2011)

120. J.-H. Prinz, B. Keller and F. Noé: “Probing molecular kinetics with Markov models: Metastable states,transition pathways and spectroscopic observables” Phys. Chem. Chem. Phys. 13, 16912-16927(2011).

121. C. Schütte, F. Noé, J. Lu, M. Sarich. and E. Vanden-Eijnden: “Markov state models based onmilestoning”, J. Chem. Phys 134, 204105 (2011).

122. T. Splettstößer, K. Holmes, F. Noé and J.C. Smith: “Construction and Simulation Analysis of anImproved Structural Model of the Actin Filament”, Proteins 79, 2033-2043 (2011)

123. H. Wu and F. Noé: “Bayesian framework for modeling diffusion processes with nonlinear drift basedon nonlinear and incomplete observations”, Phys. Rev. E 83, 036705 (2011).

124. S. Bernhard and F. Noé: “Optimal identification of semi-rigid subunits in macromolecules from mo-lecular dynamics simulation”, PLoS One 5, e10491 (2010).

125. J.D. Chodera and F. Noé: “Probability distributions of molecular observables computed from Markovmodels. II. Uncertainties in observables and their time-evolution”, J. Chem. Phys, 133, 105102(2010).

126. M. Sarich, F. Noé and C. Schütte: “On the approximation quality of Markov state models”, SIAMMultiscale Model. Simul. 8, 1154-1177 (2010).

127. H. Wu and F. Noé: “Probability Distance Based Compression of Hidden Markov Models”, SIAMMultiscale Model. Simul. 8, 1838-1861 (2010).

128. P. Metzner, F. Noé and C. Schütte: “Estimation of Transition Matrix distributions by Monte Carlosampling”, Phys. Rev. E 80, 021106 (2009).

129. F. Noé, C. Schütte, E. Vanden-Eijnden, L. Reich and T.R. Weikl: “Constructing the EquilibriumEnsemble of Folding Pathways from Short Off-Equilibrium Simulations”, Proc. Natl. Acad. Sci.USA, 106, 19011-19016 (2009).

130. T. Splettstößer, F. Noé and J.C. Smith: “Nucleotide-dependence of G-actin conformation from mul-tiple molecular dynamics simulations and observation of a putatively polymerisation-competent super-closed state”, Proteins, 67, 353-364 (2009).

131. F. Noé, I. Daidone, J.C. Smith, A. di Nola and A. Amadei: “Solvent Electrostriction-Driven PeptideFolding Revealed by Quasi-Gaussian Entropy Theory and Molecular Dynamics Simulation” J. Phys.Chem. B 112, 11155-11163 (2008).

132. F. Noé: “Probability Distributions of Molecular Observables computed from Markov Models”, J.Chem. Phys. 128, 244103 (2008).

133. F. Noé and S. Fischer: “Transition Networks for Modeling the Kinetics of Conformational Change inMacromolecules”, Curr. Opin. Struct. Biol. 18, 154-162 (2008).

134. I. Horenko, C. Hartmann, C. Schütte and F. Noé: “Data-based parameter estimation of generalizedLangevin processes”. Phys. Rev. E 76, 016706 (2007).

135. F. Noé, I. Horenko, C. Schütte and J.C. Smith: “Hierarchical Analysis of Conformational Dynamics inBiomolecules: Transition Networks of Metastable States”. J. Chem. Phys., 126, 155102 (2007).

136. P. Imhof, F. Noé, S. Fischer and J.C. Smith: “AM1/d Parameters for Magnesium in Metalloenzymes”.J. Chem. Theo. Comput. 2, 1050-1056 (2006).

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137. F. Noé, D. Krachtus, J.C. Smith and S. Fischer: “Transition Networks for the Comprehensive Charac-terization of Complex Conformational Change in Proteins.”. J. Chem. Theo. Comput. 2, 840-857(2006)

138. F. Noé, M. Oswald, G. Reinelt, J.C. Smith and S. Fischer: “Computing Best Transition Pathways inHigh-Dimensional Dynamical Systems” SIAM Multiscale Model. Sim. 5, 393-419 (2006).

139. F. Noé, J.C. Smith and S. Fischer: “Automated Computation of Low-Energy Pathways for ComplexRearrangements in Proteins: Application to the conformational switch of Ras p21.” Proteins. 59,534-544 (2005).

140. F. Noé, S.M. Schwarzl, S. Fischer and J.C. Smith: “Computational tools for analysing structuralchanges in proteins in solution.” Appl. Bioinformatics, 2, 11-17 (2003).

Refereed proceedings and book chapters

1. H. Wu, J Köhler and F. Noé. Stochastic Normalizing Flows. NeurIPS 33 (in press, preprint:arXiv:2002.06707, 2020)

2. J. Köhler, L. Klein and F. Noé: Equivariant Flows: exact likelihood generative learning for symmetricdensities. ICML 37, published in: PMLR 119 (2020)

3. F. Noé: Machine Learning for Molecular Dynamics on Long Timescales. InMachine Learning MeetsQuantum Physics, K. T. Schütt, S. Chmiela, A. von Lilienfeld, A. Tkatchenko, K. Tsuda and K.-R.Müller (Eds.), Lecture Notes in Physics, 331-372 (2020).

4. A. Mardt, L. Pasquali, F. Noé and H. Wu: Deep learning Markov and Koopman models with physicalconstraints. PMLR 107, 451-475 (2020)

5. J. Ossyra, A. Sedova, A. Tharrington, F. Noé, C. Clementi, J. C. Smith: Porting adaptive ensemblemolecular dynamics workflows to the summit supercomputer. International Conference on HighPerformance Computing, 397-417 (2019)

6. H. Wu, A. Mardt, L. Pasquali and F. Noé. Deep Generative Markov State Models NIPS 31, 3975-3984(2018)

7. H. Wu and F. Noé Spectral learning of dynamic systems from nonequilibrium data. NIPS 29, 4179-4187, (2016)

8. F. Noé: “Markov Models of Molecular Kinetics” in Encyclopedia Biophys., edited by G.C.K. Roberts,pp. 1385-1394 (Springer, Heidelberg 2013).

9. K. Faelber, S. Gao, M. Held, Y. Posor, V. Haucke, F. Noé and O. Daumke. Oligomerization ofdynamin superfamily proteins in health and disease. In: Progr. Mol. Biol. Transl. Sci. Elsevier,411-443 (2013).

10. H. Wu and F. Noé: “A flat Dirichlet process switching model for Bayesian estimation of hybridsystems”, Procedia Comput. Sci. 4, 1393-1402 (2011).

11. H. Wu and F. Noé: “Maximum a posteriori estimation for Markov chains based on Gaussian Markovrandom fields”, Procedia Comput. Sci. 1, 1659-1667 (2010).

12. C. Schütte, F. Noé, E. Meerbach, P. Metzner and C. Hartmann: “Conformation Dynamics”, In:“Proceedings of the 6th International Congress on Industrial and Applied Mathematics”. R. Jeltschand G. Wanner (Eds) 297-336, EMS publishing house (2009).

13. F. Noé, M. Oswald and G. Reinelt: “Optimization in Graphs with Limited Information on the EdgeWeights”, In: “Operations Research Proceedings 2007”. J. Kalcsics and S. Nickel (Eds) 435-440,Springer (2007).

Preprints and non-refereed Publications

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1. A. Krämer, J. Köhler and F. Noé: Training Neural Networks with Property-Preserving ParameterPerturbations arXiv:2010.07033 (2020)

2. Z. Schätzle, J. Hermann and F. Noé: Convergence to the fixed-node limit in deep variational MonteCarlo arXiv:2010.05316 (2020)

3. E. Suarez, R. P. Wiewiora, C. Wehmeyer, F. Noé, J. D. Chodera, D. M. Zuckerman: What Markovstate models can and cannot do: Correlation versus path-based observables in protein folding models.BioRxiv https://doi.org/10.1101/2020.11.09.374496 (2020).

4. N. P. Azouz, A. M. Klingler, V. Callahan, I. V. Akhrymuk, K. Elez, L. Raich, B. M. Henry, J. L. Benoit,S. W. Benoit, F. Noé, K. Kehn-Hall, M. E. Rothenberg. bioRxiv https://doi.org/10.1101/2020.05.04.077826(2020).

5. B. K. Miller, M. Geiger, T. E. Smidt and F. Noé. Relevance of Rotationally Equivariant Convolutionsfor Predicting Molecular Properties. arXiv:2008.08461 (2020)

6. M. Hoffmann, H. Hofmann-Winkler, J. C. Smith, N. Krüger, L. K. Sørensen, O. S. Søgaard, J. BoHasselstrøm, M. Winkler, T. Hempel, L. Raich, S. Olsson, T. Yamazoe, K. Yamatsuta, H. Mizuno, S.Ludwig, F. Noé, J. M. Sheltzer, M. Kjolby, S. Pöhlmann: Camostat mesylate inhibits SARS-CoV-2activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity. bioRxivhttps://doi.org/10.1101/2020.08.05.237651 (2020)

7. M. Kostré, Ch. Schütte, F. Noé and M. J. del Razo: Coupling particle-based reaction-diffusion simul-ations with reservoirs mediated by reaction-diffusion PDEs arXiv:2006.00003 (2020)

8. L. Raich, K. Meier, J. Günther, C. D. Christ, F. Noé, S Olsson. Discovery of a hidden transient statein all bromodomain families. bioRxiv https://doi.org/10.1101/2020.04.01.019547 (2020).

9. L. Sbailò, M. Dibak and F. Noé: Neural Mode Jump Monte Carlo arXiv:1912.05216 (2019)

10. M. Hoffmann, F. Noé. Generating valid Euclidean distance matrices. arXiv:1910.03131 (2019)

11. M. Sadeghi and F. Noé. First-principle hydrodynamics and kinetics for solvent-free coarse-grainedmembrane models. arXiv:1909.02722 (2019).

12. J. Köhler, L. Klein and F. Noé. Equivariant Flows: sampling configurations for multi-body systemswith symmetric energies. arXiv:1910.00753 (2019).

13. J. Hermann, Z. Schätzle and F Noé. Deep neural network solution of the electronic Schrödingerequation. arXiv:1909.08423 (2019)

14. B. Trendelkamp-Schroer, H. Wu and F. Noé: Reversible Markov chain estimation using convex-concave programming. arXiv:1603.01640 (2016).

15. F. Noé: Statistical inefficiency of Markov model count matrices. Preprint, http://publications.mi.fu-berlin.de/1699

16. G.R. Bowman, V.S. Pande and F. Noé: “Introduction and overview of this book.” In: An Introductionto Markov State Models and Their Application to Long Timescale Molecular Simulation. Advancesin Experimental Medicine and Biology, 797. Springer, pp. 1-6 (2013).

17. J.-H. Prinz, J.D. Chodera and F. Noé: “Estimation and Validation of Markov models.” In: AnIntroduction to Markov State Models and Their Application to Long Timescale Molecular Simulation.Advances in Experimental Medicine and Biology, 797. Springer, pp. 45-60 (2013).

18. F. Noé and J.D. Chodera : “Uncertainty estimation.” In: An Introduction to Markov State Modelsand Their Application to Long Timescale Molecular Simulation. Advances in Experimental Medicineand Biology, 797. Springer, pp. 61-74 (2013).

19. F. Noé and J.-H. Prinz: “Analysis of Markov models.” In: An Introduction to Markov State Modelsand Their Application to Long Timescale Molecular Simulation. Advances in Experimental Medicineand Biology, 797 . Springer, pp. 75-90 (2013).

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20. B. Keller, J.-H. Prinz and F. Noé: “Resolving the apparent gap in complexity between simulatedand measured kinetics of biomolecules” From Computational Biophysics to Systems Biology(CBSB11) Proceedings, IAS Series, 8. pp. 61-64 (2012).

21. J.D. Chodera, P.J. Elms, F. F. Noé, B. Keller, C.M. Kaiser, A. Ewall-Wice, S. Marquesee, C. Bustam-ate, N.S. Hinrichs: “Bayesian hidden Markov model analysis of single-molecule force spectroscopy:Characterizing kinetics under measurement uncertainty” arXiv:1108.1430 (2011).

22. J.D. Chodera, P.J. Elms, W.C. Swope, J.-H. Prinz, S. Marqusee, C. Bustamante, F. F. Noé, V.S.Pande “A robust approach to estimating rates from time-correlation functions” arXiv:1108.2304(2011).

23. F. Noé, J.C. Smith and C. Schütte: “A network-based approach to biomolecular dynamics”. In: “FromComputational Biophysics to Systems Biology”. U. H. E. Hansmann, J. Meinke, S. Mohanty and O.Zimmermann (Eds), 247-250, John von Neumann Institute for Computing, Juelich (2007).

24. F. Noé and J.C. Smith: “Transition Networks: A Unifying Theme for Molecular Simulation andComputer Science”. Mathematical Modeling of Biological Systems, Volume I. A. Deutsch, L.Brusch, H. Byrne, G. de Vries and H.-P. Herzel (eds). Birkhäuser, Boston, 125-144 (2007).

25. T. Becker, S. Fischer, F. Noé, A.L. Tournier, G.M. Ullmann, V. Kurkal and J.C. Smith: “Physical andfunctional aspects of protein dynamics.” In: “Soft Condensed Matter Physics in Molecular and CellBiology”, Poon & Andelman (Eds), 225-241, Taylor & Francis (2006).

26. T. Becker, S. Fischer, F. Noé, M. Ullmann, A. Tournier and J.C. Smith: “Protein Dynamics: GlassTransition and Mechanical Function.” Advances in Solid State Physics 43. Ed. B. Kramer.Springer-Verlag Heidelberg, 677-694 (2003).

Theses

1. F. Noé: “Transition Networks: Computational Methods for the Comprehensive Analysis of ComplexRearrangements in Proteins.” Ph.D. thesis, University of Heidelberg, 2006.

2. F. Noé: “The Evolution of Cell Colonies in Volvocacean Algae: Investigation by theoretical analysisand computer simulation”. Master of Science thesis, Cork Institute of Technology, Ireland (2002).

3. F. Noé: “Components for a PC-based Driver-Information System”. Bachelor of Science thesis, Uni-versity of Cooperative Education, Stuttgart, Germany (1999).

Reviewing service

Funding agencies: Deutsche Forschungsgemeinschaft (DFG), European Research Council (ERC), NationalScience Foundation (NSF), Austrian Science Fund (FWF), Swiss National Science Foundation (SNF), Neth-erlands Organisation for Scientific Research (NWO), Polish Academy of Sciences, Danish Agency for Science,Technology and Innovation, PRACE.Institutions: Polish Academy of SciencesJournals: Nat. Methods, Nat. Chem., Nat. Commun., Nat. Mater., Nat. Rev. Chem., Sci. Adv., PNAS,JACS, Phys. Rev. X, Phys. Rev. Lett., Phys. Rev. E, JMB, PCCP, JPC, JCP, JCTC, J. Comput.Chem, SIAM Multiscale Model. Simul., PLoS One, PLoS Comput. Biol., Soft Matter, Drug Discov. Today,NeurIPS, ICML, Mach. Learn. Sci. Tech.

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Selected invited presentations (> 130 total)

Nov 17, 2020 Pregl Colloquium, Ljubljiana, SVNJul 8, 2020 AI4Science kickoff workshop, Amsterdam, NLFeb 26, 2020 AI Powered Drug Discovery and Manufacturing Conference, MIT, MA, USADec 5, 2019 Keynote, Culminating workshop of Long Program “Machine learning for physics and

the physics of learning”, Institute of Pure and Applied Mathematics, UCLA, CA, USA.Sep 27, 2019 Award lecture, PHYS division, American Chemical Society Meeting, San Diego, CA,

USAMar 6, 2019 Workshop on Bayesian Statistical Inference for Biophysics, Biophysical Society

meeting, Baltimore, MD, USAMar 4, 2019 American Physical Society Meeting, Boston, MA, USADec 8, 2018 Workshop on Machine Learning for Materials and Molecules, NeurIPS 2018,

Montreal, CANApr 7, 2018 Keynote, Symposium on Biophysics Postgraduate Research HKUST, Hong Kong, CN.Mar 22, 2018 Crick Chair lecture, UC San Diego, CA, USAFeb 6, 2018 Center for Theoretical Biological Physics, Rice University, Houston, TX, USASep 15, 2017 Machine learning tutorials, IPAM workshop Complex High-Dimensional Energy

Landscapes, UC Los Angeles, CA, USASep 14, 2017 Keynote, Set-Oriented Numerics workshop, UC Santa Barbara, CA, USAMay 25, 2017 SIAM conference on applications of dynamical systems, Snowbird, UT, USAMar 14, 2017 Americal Physical Society meeting, New Orleans, LA, USAOct 24, 2016 “Collective variables in classical mechanics”, IPAM, Los Angeles, CA, USA.Oct 9, 2016 Erice school on Free Energy Landscapes, Erice, ITMar 15, 2016 American Chemical Society meeting, San Diego, USAJuly 25, 2016 Gordon Research Conference on Computational Chemistry, Girona, ESPFeb 17, 2016 CECAM workshop “Models for protein dynamics”, Lausanne, CHJan 26, 2016 Software for Exascale computing workshop, Munich, GEROct 13, 2015 CECAM workshop “Computational Modeling of Gene Expression”, Tel Aviv, ILJun 15, 2015 “Dynamics and Kinetics from Single Molecules to Cells”, Telluride, CO, USAJul 19, 2015 European Biophysical Congress, Dresden, GERSep 8, 2015 Molecular and Chemical Kinetics, Berlin, GERNov 12, 2014 BIRS workshop on particle-based reaction-diffusion dynamics, Banff, CANAug 21, 2014 CECAM workshop “Modeling cellular life”, Lausanne, CHJun 14, 2014 Coarse-graining as a Frontier of Statistical Mechanics, Santa Fe, NMMar 7, 2014 Model-Data Integration in Physical Models, Cambridge, UKOct 4, 2013 CECAM workshop “Innovative approaches in drug discovery”, Lausanne, CHSep 3, 2013 Molecular Kinetics, Berlin, GERApr 4, 2013 American Chemical Society meeting, New Orleans, USA.Nov 29, 2012 CECAM workshop “Machine learning in molecular simulation”, Lugano, CHSep 5, 2012 CECAM workshop “Protein folding dynamics”, Zürich, CHJul 19, 2011 Int’l Conference on Industrial and Applied Mathematics (ICIAM), Vancouver, CAN.Feb 26, 2011 Sanibel Symposium, University of Florida, FL, USA.Aug, 09, 2011 “Rise of the machines”, Telluride, CO, USA.Mar, 8, 2011 “Mathematical Challenges in Molecular Dynamics”, Imperial College, London, UK.Jan, 10, 2011 nDDB workshop Institut Laue-Langevin, Grenoble, FRJun 14-17, 2010 Summer school lecture series at SISSA, Trieste, ITJun 3, 2010 CECAM workshop “Complex Energy Landscapes”, Zaragoza, ESPMay 26, 2010 Leopoldina Meeting “The complexity connecting biomolecular structure and solvation

dynamics”, Bochum, GERFeb 9, 2010 W3 Professor selection symposium “Modelling and simulation of dynamics, structure

and function of complex molecular systems”, IWR HeidelbergSep 8-11, 2009 Lecture Series at National Institute of Health, Bethesda, MA, USADec 12, 2009 Plenary Speaker at “Membranes and Molecules”, Berlin, GERFeb 23, 2009 “Rare Events in High Dimensional Systems”, IPAM, Los Angeles, CA, USA

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